Strategic planning for cancer control: Utilizing machine-learning models to predict future incidences
One of the top health issues in the world is the Cancer. The number of patients with various types of cancer registered in clinics, oncology centers and hospitals are annually increasing. Predicting the estimated number of future cancer incidences for subsequent years is an important topic and needs...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Results in Control and Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720723001248 |
| _version_ | 1797615875576037376 |
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| author | Esraa Zeki Mohammed Noor Ghazi M. Jameel Ahmed Ibrahim Shukr Ahmed Ghareeb |
| author_facet | Esraa Zeki Mohammed Noor Ghazi M. Jameel Ahmed Ibrahim Shukr Ahmed Ghareeb |
| author_sort | Esraa Zeki Mohammed |
| collection | DOAJ |
| description | One of the top health issues in the world is the Cancer. The number of patients with various types of cancer registered in clinics, oncology centers and hospitals are annually increasing. Predicting the estimated number of future cancer incidences for subsequent years is an important topic and needs further study. In this paper, officially registered cases at the Ministry of Health, Iraq and Oncology Center at Kirkuk governorate are used to forecast three years ahead estimated number of incidences for different types of cancer using four machine-learning models. The considered models are; gaussian processes, multilayer perceptron (MLP), MLP regressor, and sequential minimal optimization regressor (SMOreg). The study reveals significant differences among different models based on performance metrics. Based on SMOreg analysis in 2023, most types of cancer are expected to see an increase in cases. For instance, bladder cancer is projected to rise from 13 cases in 2020 to around 33 cases in 2023, a 153.8% increase. Results show that, the prediction of incidence cases using SMOreg model is outperformed other algorithms with minimized error in most types of cancer. According to the results, there will be a rise in the number of incidences in the 2023 for most types of cancer except breast and non-hodgkin lymphoma cancer, which expected a decrease in the number of cases. By utilizing of three-year-ahead annual numbers of cancer cases predictions, governments will be able to hedge financial risks, provide patients care and plan for cancer control programs. |
| first_indexed | 2024-03-11T07:33:12Z |
| format | Article |
| id | doaj.art-ab3aceef2c524fe38c16dcc02e2796c3 |
| institution | Directory Open Access Journal |
| issn | 2666-7207 |
| language | English |
| last_indexed | 2024-03-11T07:33:12Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj.art-ab3aceef2c524fe38c16dcc02e2796c32023-11-17T05:28:26ZengElsevierResults in Control and Optimization2666-72072023-12-0113100322Strategic planning for cancer control: Utilizing machine-learning models to predict future incidencesEsraa Zeki Mohammed0Noor Ghazi M. Jameel1Ahmed Ibrahim Shukr2Ahmed Ghareeb3Informatics and Telecommunication Public Company, Ministry of Communications, Kirkuk, IraqTechnical College of Informatics, Sulaimani Polytechnic University, Sulaimani 46001, Kurdistan Region, IraqKirkuk Center for Oncology and Hematology, Ministry of Health, Kirkuk, IraqDepartment of Mechanical Engineering, College of Engineering, University of Kirkuk, Kirkuk, Iraq; Corresponding author.One of the top health issues in the world is the Cancer. The number of patients with various types of cancer registered in clinics, oncology centers and hospitals are annually increasing. Predicting the estimated number of future cancer incidences for subsequent years is an important topic and needs further study. In this paper, officially registered cases at the Ministry of Health, Iraq and Oncology Center at Kirkuk governorate are used to forecast three years ahead estimated number of incidences for different types of cancer using four machine-learning models. The considered models are; gaussian processes, multilayer perceptron (MLP), MLP regressor, and sequential minimal optimization regressor (SMOreg). The study reveals significant differences among different models based on performance metrics. Based on SMOreg analysis in 2023, most types of cancer are expected to see an increase in cases. For instance, bladder cancer is projected to rise from 13 cases in 2020 to around 33 cases in 2023, a 153.8% increase. Results show that, the prediction of incidence cases using SMOreg model is outperformed other algorithms with minimized error in most types of cancer. According to the results, there will be a rise in the number of incidences in the 2023 for most types of cancer except breast and non-hodgkin lymphoma cancer, which expected a decrease in the number of cases. By utilizing of three-year-ahead annual numbers of cancer cases predictions, governments will be able to hedge financial risks, provide patients care and plan for cancer control programs.http://www.sciencedirect.com/science/article/pii/S2666720723001248Cancer databaseTime series forecastingGaussian processesMultilayer perceptron (MLP)MLP regressorSequential minimal optimization regressor |
| spellingShingle | Esraa Zeki Mohammed Noor Ghazi M. Jameel Ahmed Ibrahim Shukr Ahmed Ghareeb Strategic planning for cancer control: Utilizing machine-learning models to predict future incidences Results in Control and Optimization Cancer database Time series forecasting Gaussian processes Multilayer perceptron (MLP) MLP regressor Sequential minimal optimization regressor |
| title | Strategic planning for cancer control: Utilizing machine-learning models to predict future incidences |
| title_full | Strategic planning for cancer control: Utilizing machine-learning models to predict future incidences |
| title_fullStr | Strategic planning for cancer control: Utilizing machine-learning models to predict future incidences |
| title_full_unstemmed | Strategic planning for cancer control: Utilizing machine-learning models to predict future incidences |
| title_short | Strategic planning for cancer control: Utilizing machine-learning models to predict future incidences |
| title_sort | strategic planning for cancer control utilizing machine learning models to predict future incidences |
| topic | Cancer database Time series forecasting Gaussian processes Multilayer perceptron (MLP) MLP regressor Sequential minimal optimization regressor |
| url | http://www.sciencedirect.com/science/article/pii/S2666720723001248 |
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